1. Field of the Invention
The present invention generally relates to image processing devices, and more particularly relates to an image processing device which converts an input binary image into a multi-valued image.
2. Description of the Related Art
Related art methods which convert a binary image into a multi-valued image include the following techniques, some of which are devised in order to improve quality of an output image.
(a) Konica Ltd., Image Lab. 1933. 3, "TECHNIQUE OF IMAGE RESTORATION FROM BINARY IMAGE INTO HALF-TONE IMAGE" discloses a method in which images to be treated are restricted to photographic (natural) images. PA1 (b) Cannon Ltd., NIP-24, "HIGH-DEFINITION MULTI-VALUED RESTORATION OF BINARY IMAGE USING NEURAL NETWORK" discloses a method which converts a binary image into a multi-valued image while distinguishing line-drawing areas from photographic(natural)-image areas by utilizing a learning capacity of a neural network. PA1 (c) Proceedings of Institute of Engineers of Electronics and Communication, Vol.J68-D, No.6, 1985, "SMOOTHING PROCESSING AND EDGE DETECTION OF DITHER IMAGE" discloses a method which detects edges and changes an aperture size of a smoothing filter used for creating a multi-valued image. PA1 (d) Proceedings of Image Electronics Conference, No.19, 1991, "METHOD OF RESTORING MULTI-VALUED IMAGE FROM BINARY IMAGE BY USING AREA IDENTIFICATION" and PA1 (e) Proceedings of Image Electronics, Vol.22, No.2, 1993, "METHOD OF RESTORING MULTI-VALUED IMAGE FROM BINARY IMAGE BY USING AREA IDENTIFICATION" disclose a method in which different types of image restoration techniques are applied based on results of image-area-separation processing of binary images, which processing identifies concentrated-pseudo-halftones by using dither matrixes. PA1 (f) Japanese Laid-Open Patent Application No.2-112966, titled "IMAGE OUTPUT METHOD AND DEVICE" discloses a method which eliminates jagged edges of letter images or line-drawing images.
Taking an advantage of a limited application to photographic images, the method (a) can demonstrate its effect to some degree. When applied to other types of images, however, the method (a) brings about an image degradation in letter images and line drawings. The method (c) which detects edges and changes the aperture size of the smoothing filter in order to preserve an edge sharpness brings about image degradation as well.
The method (b) which uses the neural network requires huge hardware, which is not desirable, and does not offer a proven effect for an unlearned image.
The methods (d) and (e) require only a small amount of hardware, since they switch the types of image processing based on results of the image-area-separation processing on binary images. However, the use of the dither matrixes for identifying concentrated-pseudo-halftones leads to a poor resolution. Also, there is a problem that erroneous-separation points can be scattered within a particular area. Thus, when the method is used for a printer device, letters or line drawings can be degraded, or photographic image areas can be partly left unchanged to multi-values. These problems make it difficult to use the method for a practical purpose.
The method (f) eliminates jagged edges of letter images or line drawings and can create problems in processing photographs and graphic images.
Accordingly, there is a need in the field of image processing devices for an image processing device which can convert a binary image into a multi-valued image of an improved image quality while eliminating jagged edges of letter images and line drawings.
Also, there is a need in the field of image output devices for an image output device which can convert a binary image into a multi-valued image of an improved image quality while eliminating jagged edges of letter images and line drawings.